Binary Quantum Particle Swarm Optimization Algorithm and Its Application to Chemical Process Fault Diagnosis

Considering fault data are absent in the real chemical production process,this paper utilizes(support) vector machines(SVM) which fits the small sample problems to diagnose the chemical process steady faults.To ensure the real-time capability of online diagnosis and eliminate the disturbances from higher (dimensional) monitored data as well as system noises,a novel fault feature selection method based on binary quantum particle swarm optimization(BQPSO) and SVM is proposed.The results of simulation prove that BQPSO can find the global optima effectively and select the fault features quickly and exactly.And,the fault diagnosis method based on SVM with feature selection can reliably diagnose the faults(online) in the complex chemical process.